Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ils15/pantheon-legacy --skill rag-pipelinesgit clone --depth 1 https://github.com/ils15/pantheon-legacyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ils15/pantheon-legacy/rag-pipelines)<a href="https://agentmods.dev/skills/ils15/pantheon-legacy/rag-pipelines"><img src="https://agentmods.dev/badge/skills/ils15/pantheon-legacy/rag-pipelines/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ils15/pantheon-legacy/rag-pipelines"><img src="https://agentmods.dev/badge/skills/ils15/pantheon-legacy/rag-pipelines.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.00872 |
| Opus 5 | $0.00012 | $0.00436 |
| Sonnet 5 | $0.00005 | $0.00174 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
rag-pipelines scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Pipelines
Retrieval-Augmented Generation pipeline design: chunking, embeddings, vector stores, retrieval strategies, and evaluation.
Pipeline Architecture
Documents → Chunk → Embed → Store → Retrieve → Generate
Chunking Strategies
| Strategy | Best For | Chunk Size |
|---|---|---|
| Fixed-size | General docs | 500-1000 tokens |
| Semantic | Long-form content | By paragraph/section |
| Code-aware | Source code | By function/class |
| Recursive | Mixed content | 1000 → 500 → 200 tokens |
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", ". ", " ", ""]
)
Embedding Models
| Model | Dimensions | Speed | Quality |
|---|---|---|---|
text-embedding-3-small |
1536 | Fast | Good |
text-embedding-3-large |
3072 | Medium | Best |
bge-large-en |
1024 | Fast | Good |
e5-large-v2 |
1024 | Fast | Good |
Vector Stores
| Store | Use Case | Scaling |
|---|---|---|
| Pinecone | Production, managed | Auto-scales |
| Weaviate | Production, self-hosted | Horizontal |
| pgvector | PostgreSQL shops | Vertical |
| Chroma | Prototyping, local | Single-node |
from langchain.vectorstores import Chroma
vector_store = Chroma.from_documents(
documents=chunks,
embedding=embeddings,
persist_directory="./chroma_db"
)
Retrieval Strategies
Similarity Search
retriever = vector_store.as_retriever(search_type="similarity", k=4)
MMR (Diversity-focused)
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 4, "lambda_mult": 0.7}
)
Hybrid (BM25 + Semantic)
from langchain.retrievers import EnsembleRetriever
retriever = EnsembleRetriever(
retrievers=[bm25_retriever, vector_retriever],
weights=[0.3, 0.7]
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 162 lines · 23 tokens per session scan A aa453b24ad20
rag-pipelines is a skill published in the GitHub repository ils15/pantheon-legacy (10 stars, last pushed 8d ago), licensed MIT. It adds 23 tokens to every session and 872 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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